Abstract: A method for managing a supply chain may include acquiring property data regarding the supply chain, acquiring one or more objectives to be achieved by the supply chain, the supply chain including a plurality of supply chain entities including customers, manufacturing facilities, and distributing facilities, and a plurality of edges connecting the supply chain entities. The method may also include establishing a first supply chain model for the supply chain in a first geographic resolution based on the property data, determining a first solution for the first supply chain model, establishing a second supply chain model for the supply chain in a second and finer geographic resolution based on the first solution, determining a second solution for the second supply chain model, manufacturing products by manufacturing facilities based on the second solution, and distributing the manufactured products by the distributing facilities and edges based on the second solution.

Abstract: A system is disclosed for configuring a supply chain using a postponement network design. The system comprises a processor configured to select a plurality of configuration center candidates for a supply chain configured to deliver products to a plurality of customers. The processor further determines one or more configuration centers based on the plurality of configuration center candidates. The configuration centers are configured to receive partially-finished products from one or more manufacturing facilities and combine the partially-finished products with components according to orders received from the customers. The processor further determines a first optimized supply chain network structure for a first portion of the supply chain, and a second optimized supply chain network structure for a second portion of the supply chain.

Abstract: A system is disclosed for adjusting network parameters for a network sensitivity analysis of a supply chain including a plurality of network entities. The system comprises a memory, one or more input/output devices, and a processor. The processor is configured to generate a supply chain network model and determine entity demands and a total network demand for the item. The processor is further configured to determine a total network demand change and iteratively assign a portion of the total network demand change to at least one network entity until all of the total network demand change is assigned. The processor is further configured to adjust the entity demand at the at least one network entity, modify the supply chain network model, analyze sensitivity of the supply chain network model, and configure the supply chain based on the analysis.

Abstract: A system is disclosed for reducing oscillation in a supply chain, including a memory configured to store instructions, one or more input/output devices configured to receive user inputs and generate a user interface, and a processor configured to receive and execute the instructions. The instructions cause the processor to determine a plurality of network parameters for the supply chain for supplying a plurality of product lines from a plurality of sources to a plurality of customers. The instructions further cause the processor to transform the network parameters from a real space to a neutral space according to a common basis, and determine a neutral-space optimized network model for the supply chain based on neutral-space representations of the network parameters. The instructions further cause the processor to determine real-space supply-demand relationships based on the neutral-space optimized network model, and configure supply chain based on the real-space supply-demand relationships.

Abstract: A computer-implemented forecasting method is disclosed. The method may include collecting a plurality of historical demand streams. Each historical demand stream may include a time series of historical demand data for a target item at a product distribution node in a supply chain. The method may also include constructing a model tree for the plurality of historical demand streams based on a plurality of forecasting models. The method may further include forecasting a future demand stream of a target item to be distributed by a product distribution node by using a forecasting model that is associated with a specific subgroup that includes a historical demand stream of the target item at the product distribution node.

Abstract: A computer-implemented method for analyzing supply chain sensitivity based on a supply chain model is described. The method comprises defining a plurality of input parameters and an objective for the supply chain model Each of the input parameters includes a plurality of values within a respective range. The method further comprises obtaining a plurality of configurations of the supply chain for achieving the objective by applying the values of the input parameters to the supply chain model and generating a graphical representation for the objective based on the configurations of the supply chain. The graphical representation includes a plurality of data points, each of the data points representing a configuration of the supply chain.

Abstract: The present disclosure is directed to a computer-implemented method for designing a supply chain for multiple objectives based on a supply chain model. The method comprises defining a plurality of objectives for the supply chain model. The supply chain model includes a plurality of edges connecting one or more demands with one or more supplies. The method further includes selecting a first one of the plurality of objectives and at least a second one of the plurality of objectives and configuring, using a controller, the supply chain model for the first objective. The method further comprises identifying, using the controller, a subset of the plurality of edges based on the supply chain model configured for the first objective and relationships between the plurality of edges and the at least second objective. The method further comprises operating the supply chain according to the identified subset of the plurality of edges.

Abstract: A computer-implemented method is disclosed for forecasting characteristics of a target item. The method may include collecting a plurality of forecasting equations, decomposing each of the plurality of forecasting equations into one or more building blocks, and creating a population table consisting of a plurality of candidate equations each including one or more of the building blocks. The method may also include evolving the candidate equations in the population table by implementing a genetic algorithm based on historical characteristic data of the target item over a period of time until a convergence criterion is satisfied. The method may further include selecting an equation from the plurality of candidate equations in the population table that best fits the historical characteristic data over the period of time, and forecasting the characteristics of the target item by using the selected equation.

Abstract: A computer-implemented method for managing a supply chain including a central distribution center (DC) that distributes products to one or more edge DCs. The method may include determining, by one or more processors, a first rate of change of future demand for a product distributed by the edge DC over a predetermined future time horizon, and a second rate of change of historical demand for the product distributed by the edge DC over a historical time period. The method may also include updating flow of customer orders and storage space requirements for the central DC and the edge DC based on a difference between the first rate and the second rate.

Abstract: A control system is disclosed. The control system may have a physical sensor that may output a physical output value, and a virtual sensor network that may output a virtual output value. The control system may also have an electronic control module that may be configured to receive the physical output value from the physical sensor and receive the virtual output value from the virtual sensor. The electronic control module may be further configured to determine a trust level of the physical sensor based on the physical output value, and determine that the trust level of the physical sensor is below a first threshold trust value. The electronic control module may control a machine based on the virtual output value responsive to determining that the trust level of the physical sensor is below the first threshold trust value.

Abstract: A computer-implemented method for managing a supply chain including a plurality of supply chain entities is disclosed. The method may include determining a plurality of input parameters for modeling the supply chain. Each input parameter has a plurality of input parameter values within a plausible range. The method may also include determining a plurality of candidate network structures, and determining a business goal value for each candidate network structure based on a plurality of possible input combinations of the input parameter values. The method may further include determining a statistical distribution of the business goal values for each network structure.

Abstract: A characteristic forecasting system is disclosed. The characteristic forecasting system may have a memory and a processor. The memory may store instructions, that, when executed, enable the processor to generate at least one chromosome using a genetic algorithm, the chromosome including data values for variables of one or more equations used to generate forecast data for a target item. The processor may also be enabled to calculate a chromosome value for the chromosome based on a goal function associated with the genetic algorithm and determine at least one process parameter value for the chromosome at a time interval of the forecast data. The processor may also compare the process parameter value to a process constraint value representing a process limitation associated with the target item and modify the chromosome value for the chromosome responsive to a determination that the process parameter value does not satisfy the process constraint value.

Abstract: A method for managing a logistics network is disclosed. The method may include receiving, by a processor, historical demand data representing historical demand for an item at each one of a plurality of facilities associated with the logistics network, the plurality of facilities including at least one manufacturing facility and at least one distributing facility; forecasting future demand data representing a forecasted future demand for the item at each one of the plurality of facilities based on the respective historical demand data and one or more respective business goals for each facility; adjusting the future demand data at each one of the facilities by compensating for a shipping time delay associated with shipping the item from a supplier to the facility; and combining the adjusted future demand data at each one of the facilities to generate combined future demand data representing demand for the item at the supplier.

Abstract: A computer-implemented method for forecasting characteristics of a target item is disclosed. The method may include determining a forecast function representing characteristics of the target item. The forecast function may include one or more generic functions and one or more customer-specific functions. Each one of the generic functions may be uniform for a plurality of customers within one or more customer groups and may have one or more generic function variables. Each one of the customer-specific functions may be specifically configured for a corresponding customer group and may have one or more customer-specific function variables. The method may also include determining a data value for each one of the generic function variables and the customer-specific function variables by using a genetic algorithm. The method may further include forecasting the characteristics of the target item by using the forecast function and the determined data values.

Abstract: A control system is disclosed. The control system may have a physical sensor configured to measure physical parameter values of a machine. The control system may also have a virtual sensor network system configured to receive the physical parameter values measured by the physical sensor as input parameter values, and generate output parameter values based on the input parameter values. Further, the control system may have an electronic control module configured to store an output parameter value default rate of change and an output parameter value threshold rate of change, compare a rate of change of the output parameter values generated by the virtual sensor network system to the output parameter value threshold rate of change, and control the machine based on the output parameter value default rate of change if the rate of change of the output parameter values exceeds the output parameter value threshold rate of change.

Abstract: A characteristic forecasting system is disclosed. The characteristic forecasting system may have a memory module and a processor. The memory module may store instructions, that, when executed, enable the processor to generate forecast data for a target item based on historical data related to the target item, the forecast data representing a forecast for characteristics of the target item. The processor may also be enabled to analyze a subset of the historical data within an observation window to determine whether the forecast data should be updated with new forecast data and generate the new forecast data for the target item responsive to a determination that the forecast data should be updated with the new forecast data.

Abstract: A computer-implemented method for simulating a facility to achieve one or more business goals is disclosed. The method includes receiving, by a processor, one or more input parameters for a discrete event simulator (DES) module for a facility, a plurality of business goals for the facility, and a selection of a goal optimization technique. The method also includes generating, by the processor, a DES output that simulates processes occurring within the facility based on the one or more input parameters and simultaneously achieves the business goals in accordance with the selected goal optimization technique.

Abstract: A computer-implemented method for simulating movement of materials within a facility using predefined software objects is disclosed. The method includes storing a plurality of predefined software objects in a computer memory, each of the predefined software objects including one or more discrete event simulator commands and representing a process performed at a facility. The method also includes receiving, by one or more processors, an instruction to simulate movement of a material within a facility, and linking, by the one or more processors, two or more of the predefined software objects together to represent the movement of the material within the facility. The method further includes executing, by the one or more processors, the linked predefined software objects using a discrete event simulator to simulate the movement of the material within the facility.

Abstract: A sensor error detection and compensation system is disclosed. The system may have a sensor state estimation module that is configured to generate a physical sensor confidence value representing an accuracy estimation of a physical sensor output value received from a physical sensor. The system may also have a sensor output aggregation module configured to determine an aggregated sensor value based on the physical sensor confidence value, the physical sensor output value, a virtual sensor output value received from a virtual sensor, and a virtual sensor confidence value representing an accuracy estimation of the virtual sensor output value. Moreover, the system may have a replace sensor decision module configured to determine whether the physical sensor has failed by comparing the physical sensor confidence value to a replacement threshold level.

Abstract: A characteristic forecasting system is disclosed. The characteristic forecasting system may have a memory module and a processor. The memory module may store instructions, that, when executed, enable the processor to generate a forecast function that represents forecasted characteristics of a target item and includes one or more continuous functions and one or more attenuating functions. Each of the attenuating functions may attenuate forecast values generated by the continuous functions at periodic time intervals. The processor may also be enabled to forecast the characteristics of the target item using the forecast function.